


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						0
    __magic_name__         =						False
    __magic_name__         =						3.0






class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()     ,   {}       )
         self.assertDictEqual(MockClass(a=2       ).to_kwargs()     ,   {'''a''': 2}       )
         self.assertDictEqual(MockClass(a=2     ,   b=__snake_case       ).to_kwargs()     ,   {'''a''': 2, '''b''': True}       )
         self.assertDictEqual(MockClass(a=2     ,   c=2.25       ).to_kwargs()     ,   {'''a''': 2, '''c''': 2.25}       )
    @require_cuda
    def 						a_     (			self       ):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         snake_case											=							GradScalerKwargs(init_scale=1_0_2_4     ,   growth_factor=2       )
         AcceleratorState._reset_state()
         snake_case											=							Accelerator(mixed_precision='''fp16'''     ,   kwargs_handlers=[scaler_handler]       )
         print(accelerator.use_fpaa       )
         snake_case											=							accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale     ,   1024.0       )
         self.assertEqual(scaler._growth_factor     ,   2.0       )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor     ,   0.5       )
         self.assertEqual(scaler._growth_interval     ,   2_0_0_0       )
         self.assertEqual(scaler._enabled     ,   __snake_case       )




    @require_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__       )]
         execute_subprocess_async(__snake_case     ,   env=os.environ.copy()       )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					int      =       DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _SCREAMING_SNAKE_CASE     :					List[Any]      =       Accelerator(kwargs_handlers=[ddp_scaler])
 _SCREAMING_SNAKE_CASE     :					int      =       torch.nn.Linear(1_00, 2_00)
 _SCREAMING_SNAKE_CASE     :					str      =       accelerator.prepare(model)

 # Check the values changed in kwargs
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       ""
 _SCREAMING_SNAKE_CASE     :					int      =       model.bucket_bytes_cap // (10_24 * 10_24)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




